Electric vehicle charging in stochastic smart microgrid operation with fuel cell and RES units. (23rd March 2017)

• Record Type: Journal Article
• Authors: Anastasiadis, Anestis G.; Konstantinopoulos, Stavros; Kondylis, Georgios P.; Vokas, Georgios A.
• Is Part Of: International journal of hydrogen energy. Volume 42:Number 12(2017)
• Page Start: 8242
• Page End: 8254
• Publication Date: 2017-03-23
• Subjects: Fuel cell -- Renewable energy sources -- Distributed energy resources -- Stochastic optimal charging -- Electric vehicles -- Smart microgrid; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2017.01.208
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Plug-in electric vehicles increasingly augment their share in the global market as they appear to be an economic and emission-free alternative to modern means of transportation. As their presence strengthens, ways that will ensure economic charge along with uninterrupted grid operation are necessary to be found. This paper aims to approach the economic optimization problem that includes several Electric Vehicles (EVs) within a Low Voltage (LV) network comprising various Distributed Energy Resources (DER) as fuel cell, Renewable Energy Sources (RES), (photovoltaics, wind turbine) etc. via a scenario based simulation. The purpose is to investigate the main variables of the grid, such as its operating cost, charging patterns, power injection from the upstream network, resulting from the coordinated control of DER in Smart Microgrid operation in conjunction to the flexible load the controlled EV charging introduces. The base case study is that of absence of EVs, and therefore the demand is met only by the upstream network and the DER units. Subsequently, EVs are introduced as controllable loads and finally as dispatchable storage units incorporating a Vehicle to Grid (V2G) capability to the Smart Microgrid. Furthermore, the problem is not tackled deterministically and although forecasts for all network parameters are assumed to be known, forecasting errors and stochastic driver patterns cannot be ignored. Thus, for each imposed policy, a scenario based approach is implemented to determine operating cost in various cases along to DER utilization and the effect EVs bear on these results. Highlights: Harmony Search is used to optimize the penetration of the EVs into the Microgrid. Stochastic modeling of Microgrid's parameters. Valley filling when the charging of the EVs is controlled. Vehicle to Grid capabilities leads to further cost reduction.